Abstract
INTRODUCTION: Cardiovascular autonomic neuropathy (CAN) is a severe complication of diabetes that impairs the regulation of the cardiovascular system. This can cause hemodynamic instability, arrhythmias, and silent ischemia. Despite its clinical significance, routine testing is not widely implemented. Therefore, this study investigated the performance of a novel screening tool, the ProCVT smart-sheet, based on electrocardiography (ECG)-derived cardiac vagal tone (CVT), compared to standardized methods in type 2 diabetes (T2D). METHODS: Forty individuals with T2D (aged 45-75) with varying degrees of CAN and 20 age-matched controls were included in this cross-sectional study. Autonomic profiling included cardiovascular autonomic reflex tests, short-term CVT, 72-hour blood pressure monitoring, and three nights of home monitoring with the ProCVT smart-sheet. Receiver operating characteristics assessed the performance of long-term and short-term CVT to detect any, early-stage, and manifest CAN. RESULTS: A total of 164 recordings were obtained, with an average of 93% of each recording classified as very high signal quality before artifact removal. Short- and long-term mean CVT were the best-performing parameters, identifying any and manifest CAN with AUCs of 0.64-0.79. Suggested cut-offs were 2.7 linear vagal scale (LVS) units for short-term and 5.0 LVS for long-term recordings. CONCLUSION: The ProCVT smart-sheet offers a feasible, non-invasive alternative to traditional ECGs that rely on surface electrodes. CVT shows promise as a biomarker for identifying manifest CAN in T2D. However, long-term recordings of CVT were not superior to short-term recordings. Further research is warranted to assess its value in the detection of early-stage CAN.